PREDICTING SOCIAL NETWORK ADDICTION USING VARIANT SIGMOID TRANSFER FEED-FORWARD  NEURAL NETWORKS (FNN-SNA)

Researchers have reflected on personal traits that may predict Social Networking Sites (SNS) addiction. However, most of the researchers involved in the findings of personality traits predictor for social networking addiction either postulate or based their conclusions on analytical tools. Moreso, a review of the literature reveals that the prediction of social networking addiction using classifiers have not been well researched. We examined the prediction of SNS addiction from a well-structured questionnaire consisting of sixteen (16) personality traits. The questionnaire was administered on the google form with a response rate of 95% out of the 102-sample size. Additionally, a three (3) variant sigmoid transfer feed- forward neural networks was developed for the prediction of SNS addiction. Result indicated that pertinence (β = 0.251, p 0.01) was the most powerful predictor of social networking addiction in general and less obscurity addiction (β = 0.244, p 0.01). Experimental results also showed that the developed classifier correctly predict SNS addiction with 98% accuracy compared to similar classifiers.

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Social Advertisements and Recall

Encyclopedia of Organizational Knowledge, Administration, and Technology - Advances in Logistics, Operations, and Management Science ◽

10.4018/978-1-7998-3473-1.ch152 ◽

2021 ◽

pp. 2197-2207

Author(s):

Anish Yousaf ◽

Roktim Sarmah

Keyword(s):

Social Networking ◽

Social Networking Sites ◽

Exploratory Study ◽

Response Rate ◽

Study Data ◽

State Governments ◽

Policy Makers ◽

High Recall ◽

Pulse Polio ◽

Structured Questionnaire

Researches in the context of social advertisement are carried out in European nations with few exceptions from India where various social advertisement campaigns are run by central as well as state governments. Current study is an attempt towards measuring recall of popular social advertisement campaigns in India and to explore the reasons for the same using an exploratory study. Data was collected using a structured questionnaire. A total of 400 respondents participate in the study with a response rate of 86%. Findings revealed that Swachh Bharat Abhiyaan was the most recalled social advertisement campaign followed by Pulse Polio Abhiyan and Cancer advertisement campaign. Result revealed that social advertisements promoted using celebrity(ies) and politicians have more impact and high recall. It was also found that social advertisements using television and social networking sites as media tools are widely accepted among youths. Findings of the study will be helpful for policy makers who can use the findings to promote various social advertisement campaigns.

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Development of Sorting System for Fishes by Feed-forward Neural Networks Using Rotation Invariant Features

IEEJ Transactions on Electronics Information and Systems ◽

10.1541/ieejeiss.131.404 ◽

2011 ◽

Vol 131 (2) ◽

pp. 404-410 ◽

Cited By ~ 1

Author(s):

Yuhki Shiraishi ◽

Fumiaki Takeda

Keyword(s):

Neural Networks ◽

Rotation Invariant ◽

Feed Forward ◽

Invariant Features ◽

Feed Forward Neural Networks ◽

Sorting System

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Air quality prediction in Milan: feed-forward neural networks, pruned neural networks and lazy learning

Ecological Modelling ◽

10.1016/j.ecolmodel.2005.01.008 ◽

2005 ◽

Vol 185 (2-4) ◽

pp. 513-529 ◽

Cited By ~ 126

Author(s):

Giorgio Corani

Keyword(s):

Neural Networks ◽

Air Quality ◽

Quality Prediction ◽

Lazy Learning ◽

Feed Forward ◽

Air Quality Prediction ◽

Feed Forward Neural Networks

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An Evolutionary Approach to the Use of Neural Networks in the Segmentation of Handwritten Numerals

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001497000317 ◽

1997 ◽

Vol 11 (05) ◽

pp. 717-734 ◽

Cited By ~ 1

Author(s):

J. M. Westall ◽

M. S. Narasimha

Keyword(s):

Neural Networks ◽

Production System ◽

Background Noise ◽

Evolutionary Approach ◽

Feed Forward ◽

Feed Forward Neural Networks ◽

Decision Functions

Neural networks are now widely and successfully used in the recognition of handwritten numerals. Despite their wide use in recognition, neural networks have not seen widespread use in segmentation. Segmentation can be extremely difficult in the presence of connected numerals, fragmented numerals, and background noise, and its failure is a principal cause of rejected and incorrectly read documents. Therefore, strategies leading to the successful application of neural technologies to segmentation are likely to yield important performance benefits. In this paper we identify problems that have impeded the use of neural networks in segmentation and describe an evolutionary approach to applying neural networks in segmentation. Our approach, based upon the use of monotonic fuzzy valued decision functions computed by feed-forward neural networks, has been successfully employed in a production system.

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